Dealing with artifact-centric systems: a process mining approach

Process mining provides a series of techniques to analyze business processes based on execution data in enterprises. It has been successfully applied to classical processes on WFM/BPM systems, in which one process execution consists of events attached with the same case id. However, existing process mining techniques suffer from problems when dealing with artifact-centric systems, such as ERP and CRM, in which a business process involves a set of interacting artifacts and a case notion for the whole process is missing. Some typical problems are convergence and divergence in XES logs, and lost interactions between multiple instances in process models. Existing artifact-centric approaches try to address these problems, but have not yet solved them satisfactorily. For instance, one has to pick an instance notion in each artifact, the description of the end-to-end behavior is distributed over multiple diagrams, and the interactions between the data perspective and the behavioral perspective are not explicitly presented. This paper proposes a set of new techniques, such as a novel log format and a novel modeling language, to enable process mining for artifact-centric systems.